Recently, the Korean SAT and the grade-based evaluation of enterprises have been changed into a real English performance evaluation depending on speaking and writing.
That is, the Ministry of Education has developed NEAT (National English Ability Test, which is an Internet-based evaluation of listening, reading comprehension, speaking and writing) and is enforcing it by demonstration, and plans to replace the civil service examination and an English test of the SAT. In such an English performance evaluation, an automatic composition evaluation system is introduced for evaluating writing ability.
The automatic composition evaluation system grammatically analyzes a composed sentence and evaluates it through error detection whether or not it is composed grammatically. In this case, in the process of analyzing the composed sentence, it is necessarily required to have processes of dividing the sentence into morphemes, tagging a part of speech to each morpheme and performing a syntax analysis. However, a conventional syntax analysis does not consider a vocabulary, a part of speech, a meaning and a context correlation complexly, but only depends on part-of-speech string information, whereby there is a problem that the accuracy thereof is significantly decreased.
In particular, since a natural language has a variety of phenomena having interdependent relationships in context, a generally defined grammar has a limitation in a syntax analysis. For instance, in a case that a word is to be parsed into a ‘verb’ through a syntax analysis, it could be parsed into not a ‘verb’ but a ‘noun’ due to the part of speech of the adjacent word. In this case, there is a problem in which two or more syntax trees are extracted through a syntax analysis and the syntax analysis thereof becomes ambiguous.
Accordingly, there is a need to have a solution to enhance an automatic evaluation system by performing a correct syntax analysis on a connection relationship between adjacent parts of speech of a tagged input sentence.